Attribute value of reconstructed position associated with plural original points

ABSTRACT

Aspects of the disclosure provide methods and apparatuses for point cloud compression. In some examples, an apparatus for point cloud compression includes processing circuitry. The processing circuitry determines a plurality of original points in a point cloud that is quantized to a reconstructed position. The processing circuitry determines an attribute value of the reconstructed position based on attribute information of the plurality of original points. Further, the processing circuitry encodes the point cloud based on the determined attribute value of the reconstructed position.

INCORPORATION BY REFERENCE

The present application is a continuation of U.S. Ser. No. 17/064,029,filed Oct. 6, 2020, which claims the benefit of priority to U.S.Provisional Application No. 62/942,536, “FAST RECOLOR FOR POINT CLOUDCODING” filed on Dec. 2, 2019. The disclosures of the prior applicationsare hereby incorporated by reference in their entirety.

TECHNICAL FIELD

The present disclosure describes embodiments generally related to pointcloud coding.

BACKGROUND

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent the work is described in thisbackground section, as well as aspects of the description that may nototherwise qualify as prior art at the time of filing, are neitherexpressly nor impliedly admitted as prior art against the presentdisclosure.

Various technologies are developed to capture and represent the world,such as objects in the world, environments in the world, and the like in3-dimensional (3D) space. 3D representations of the world can enablemore immersive forms of interaction and communication. Point clouds canbe used as a 3D representation of the world. A point cloud is a set ofpoints in a 3D space, each with associated attributes, e.g. color,material properties, texture information, intensity attributes,reflectivity attributes, motion related attributes, modality attributes,and various other attributes. Such point clouds may include largeamounts of data and may be costly and time-consuming to store andtransmit.

SUMMARY

Aspects of the disclosure provide methods and apparatuses for pointcloud compression and decompression. In some examples, an apparatus forpoint cloud compression/decompression includes processing circuitry. Insome embodiments, the processing circuitry determines one or moreoriginal points in a point cloud that are associated with areconstructed position. Positions of the one or more original points canbe reconstructed, according to a geometry quantization, to thereconstructed position. The processing circuitry then determines anattribute value for the reconstructed position based on attributeinformation of the one or more original points, and encodes texture ofthe point cloud with the reconstructed position having the determinedattribute value.

In some embodiments, the apparatus includes a memory storing a datastructure that associates the one or more original points with thereconstructed position. The data structure is accessed based on thereconstructed position to retrieve the one or more original pointsassociated with the reconstructed position.

In some embodiments, the processing circuitry performs an octreepartition that partitions a space of the point cloud into voxels duringgeometry quantization, and associates the one or more original pointsthat are positioned in a voxel with the reconstructed position forrepresenting the voxel. In an example, the association relationship isstored using a suitable data structure.

In some embodiments, the processing circuitry calculates an average ofattribute values of multiple original points as the determined attributevalue for the reconstructed position in response to the multipleoriginal points being associated with the reconstructed position. Insome examples, the processing circuitry calculates a weighted average ofthe attribute values of the multiple original points as the determinedattribute value for the reconstructed position. In an example, theprocessing circuitry weights an attribute value of an original point inthe multiple original points based on an inverse of a distance betweenthe original point and the reconstructed position.

In some embodiments, the processing circuitry assigns a specificattribute value of a nearest point in multiple original points to be thedetermined attribute value for the reconstructed point in response tothe multiple original points being associated with the reconstructedposition. In an example, the processing circuitry selects a medianattribute value among attribute values for nearest points in response tothe nearest points in the multiple original points having a sameshortest distance to the reconstructed position. In another example, theprocessing circuitry calculates a mean attribute value of attributevalues for nearest points in response to the nearest points in themultiple original points having a same shortest distance to thereconstructed position.

Aspects of the disclosure also provide a non-transitorycomputer-readable medium storing instructions which when executed by acomputer for point cloud encoding/decoding cause the computer to performany one or a combination of the methods for point cloudencoding/decoding.

BRIEF DESCRIPTION OF THE DRAWINGS

Further features, the nature, and various advantages of the disclosedsubject matter will be more apparent from the following detaileddescription and the accompanying drawings in which:

FIG. 1 is a schematic illustration of a simplified block diagram of acommunication system in accordance with an embodiment;

FIG. 2 is a schematic illustration of a simplified block diagram of astreaming system in accordance with an embodiment;

FIG. 3 shows a block diagram of an encoder for encoding point cloudframes, according to some embodiments;

FIG. 4 shows a block diagram of a decoder for decoding a compressedbitstream corresponding to point cloud frames according to someembodiments;

FIG. 5 is a schematic illustration of a simplified block diagram of avideo decoder in accordance with an embodiment;

FIG. 6 is a schematic illustration of a simplified block diagram of avideo encoder in accordance with an embodiment;

FIG. 7 shows a block diagram of an encoder for encoding point cloudframes, according to some embodiments;

FIG. 8 shows a block diagram of a decoder for decoding a compressedbitstream corresponding to point cloud frames according to someembodiments;

FIG. 9 shows a diagram illustrating a partition of a cube based on theoctree partition technique according to some embodiments of thedisclosure.

FIG. 10 shows an example of an octree partition and an octree structurecorresponding to the octree partition according to some embodiments ofthe disclosure.

FIG. 11 shows a diagram illustrating derivation techniques according tosome embodiment of the disclosure.

FIG. 12 shows a flow chart outlining a process example in accordancewith some embodiments.

FIG. 13 is a schematic illustration of a computer system in accordancewith an embodiment.

DETAILED DESCRIPTION OF EMBODIMENTS

Aspects of the disclosure provide point cloud coding (PCC) techniques.PCC can be performed according to various schemes, such as ageometry-based scheme that is referred to as G-PCC, a video coding basedscheme that is referred to as V-PCC, and the like. According to someaspects of the disclosure, the G-PCC encodes the 3D geometry directlyand is a purely geometry-based approach without much to share with videocoding, and the V-PCC is heavily based on video coding. For example,V-PCC can map a point of the 3D cloud to a pixel of a 2D grid (animage). The V-PCC scheme can utilize generic video codecs for pointcloud compression. Moving picture experts group (MPEG) is working onG-PCC standard and V-PCC standard that respectively using the G-PCCscheme and the V-PCC scheme.

Aspects of the disclosure provide recoloring techniques for a PCCscheme, such as the G-PCC scheme and the V-PCC scheme. The recoloringtechniques can improve coding speed for point cloud.

Point Clouds can be widely used in many applications. For example, pointclouds can be used in autonomous driving vehicles for object detectionand localization; point clouds can be used in geographic informationsystems (GIS) for mapping, and can be used in cultural heritage tovisualize and archive cultural heritage objects and collections, etc.

Hereinafter, a point cloud generally may refer to a set of points in a3D space, each point can be defined by position information withassociated attributes, e.g. color, material properties, textureinformation, intensity attributes, reflectivity attributes, motionrelated attributes, modality attributes, and various other attributes.Point clouds can be used to reconstruct an object or a scene as acomposition of such points. The points can be captured using multiplecameras, depth sensors or Lidar in various setups and may be made up ofthousands up to billions of points in order to realistically representreconstructed scenes. A patch generally may refer to a contiguous subsetof the surface described by the point cloud. In an example, a patchincludes points with surface normal vectors that deviate from oneanother less than a threshold amount.

Compression technologies can reduce the amount of data required torepresent a point cloud for faster transmission or reduction of storage.As such, technologies are needed for lossy compression of point cloudsfor use in real-time communications and six Degrees of Freedom (6 DoF)virtual reality. In addition, technology is sought for lossless pointcloud compression in the context of dynamic mapping for autonomousdriving and cultural heritage applications, and the like.

According to an aspect of the disclosure, the main philosophy behindV-PCC is to leverage existing video codecs to compress the geometry,occupancy, and texture of a dynamic point cloud as three separate videosequences. The extra metadata needed to interpret the three videosequences are compressed separately. A small portion of the overallbitstream is the metadata, which could be encoded/decoded efficientlyusing software implementation. The bulk of the information is handled bythe video codec.

FIG. 1 illustrates a simplified block diagram of a communication system(100) according to an embodiment of the present disclosure. Thecommunication system (100) includes a plurality of terminal devices thatcan communicate with each other, via, for example, a network (150). Forexample, the communication system (100) includes a pair of terminaldevices (110) and (120) interconnected via the network (150). In theFIG. 1 example, the first pair of terminal devices (110) and (120) mayperform unidirectional transmission of point cloud data. For example,the terminal device (110) may compress a point cloud (e.g., pointsrepresenting a structure) that is captured by a sensor (105) connectedwith the terminal device (110). The compressed point cloud can betransmitted, for example in the form of a bitstream, to the otherterminal device (120) via the network (150). The terminal device (120)may receive the compressed point cloud from the network (150),decompress the bitstream to reconstruct the point cloud, and suitablydisplay the reconstructed point cloud. Unidirectional data transmissionmay be common in media serving applications and the like.

In the FIG. 1 example, the terminal devices (110) and (120) may beillustrated as servers, and personal computers, but the principles ofthe present disclosure may be not so limited. Embodiments of the presentdisclosure find application with laptop computers, tablet computers,smart phones, gaming terminals, media players, and/or dedicatedthree-dimensional (3D) equipment. The network (150) represents anynumber of networks that transmit compressed point cloud between theterminal devices (110) and (120). The network (150) can include forexample wireline (wired) and/or wireless communication networks. Thenetwork (150) may exchange data in circuit-switched and/orpacket-switched channels. Representative networks includetelecommunications networks, local area networks, wide area networks,and/or the Internet. For the purposes of the present discussion, thearchitecture and topology of the network (150) may be immaterial to theoperation of the present disclosure unless explained herein below.

FIG. 2 illustrates a simplified block diagram of a streaming system(200) in accordance with an embodiment. The FIG. 2 example is anapplication for the disclosed subject matter for a point cloud. Thedisclosed subject matter can be equally applicable to other point cloudenabled applications, such as, 3D telepresence application, virtualreality application, and the like.

The streaming system (200) may include a capture subsystem (213). Thecapture subsystem (213) can include a point cloud source (201), forexample light detection and ranging (LIDAR) systems, 3D cameras, 3Dscanners, a graphics generation component that generates theuncompressed point cloud in software, and the like that generates forexample point clouds (202) that are uncompressed. In an example, thepoint clouds (202) include points that are captured by the 3D cameras.The point clouds (202), depicted as a bold line to emphasize a high datavolume when compared to compressed point clouds (204) (a bitstream ofcompressed point clouds). The compressed point clouds (204) can begenerated by an electronic device (220) that includes an encoder (203)coupled to the point cloud source (201). The encoder (203) can includehardware, software, or a combination thereof to enable or implementaspects of the disclosed subject matter as described in more detailbelow. The compressed point clouds (204) (or bitstream of compressedpoint clouds (204)), depicted as a thin line to emphasize the lower datavolume when compared to the stream of point clouds (202), can be storedon a streaming server (205) for future use. One or more streaming clientsubsystems, such as client subsystems (206) and (208) in FIG. 2 canaccess the streaming server (205) to retrieve copies (207) and (209) ofthe compressed point cloud (204). A client subsystem (206) can include adecoder (210), for example, in an electronic device (230). The decoder(210) decodes the incoming copy (207) of the compressed point clouds andcreates an outgoing stream of reconstructed point clouds (211) that canbe rendered on a rendering device (212).

It is noted that the electronic devices (220) and (230) can includeother components (not shown). For example, the electronic device (220)can include a decoder (not shown) and the electronic device (230) caninclude an encoder (not shown) as well.

In some streaming systems, the compressed point clouds (204), (207), and(209) (e.g., bitstreams of compressed point clouds) can be compressedaccording to certain standards. In some examples, video coding standardsare used in the compression of point clouds. Examples of those standardsinclude, High Efficiency Video Coding (HEVC), Versatile Video Coding(VVC), and the like.

FIG. 3 shows a block diagram of a V-PCC encoder (300) for encoding pointcloud frames, according to some embodiments. In some embodiments, theV-PCC encoder (300) can be used in the communication system (100) andstreaming system (200). For example, the encoder (203) can be configuredand operate in a similar manner as the V-PCC encoder (300).

The V-PCC encoder (300) receives point cloud frames as uncompressedinputs and generates bitstream corresponding to compressed point cloudframes. In some embodiments, the V-PCC encoder (300) may receive thepoint cloud frames from a point cloud source, such as the point cloudsource (201) and the like.

In the FIG. 3 example, the V-PCC encoder (300) includes a patchgeneration module (306), a patch packing module (308), a geometry imagegeneration module (310), a texture image generation module (312), apatch info module (304), an occupancy map module (314), a smoothingmodule (336), image padding modules (316) and (318), a group dilationmodule (320), video compression modules (322), (323) and (332), anauxiliary patch info compression module (338), an entropy compressionmodule (334), and a multiplexer (324).

According to an aspect of the disclosure, the V-PCC encoder (300),converts 3D point cloud frames into an image-based representation alongwith some meta data (e.g., occupancy map and patch info) that is used toconvert the compressed point cloud back into a decompressed point cloud.In some examples, the V-PCC encoder (300) can convert 3D point cloudframes into geometry images, texture images and occupancy maps, and thenuse video coding techniques to encode the geometry images, textureimages and occupancy maps into a bitstream. Generally, a geometry imageis a 2D image with pixels filled with geometry values associated withpoints projected to the pixels, and a pixel filled with a geometry valuecan be referred to as a geometry sample. A texture image is a 2D imagewith pixels filled with texture values associated with points projectedto the pixels, and a pixel filled with a texture value can be referredto as a texture sample. An occupancy map is a 2D image with pixelsfilled with values that indicate occupied or unoccupied by patches.

The patch generation module (306) segments a point cloud into a set ofpatches (e.g., a patch is defined as a contiguous subset of the surfacedescribed by the point cloud), which may be overlapping or not, suchthat each patch may be described by a depth field with respect to aplane in 2D space. In some embodiments, the patch generation module(306) aims at decomposing the point cloud into a minimum number ofpatches with smooth boundaries, while also minimizing the reconstructionerror.

The patch info module (304) can collect the patch information thatindicates sizes and shapes of the patches. In some examples, the patchinformation can be packed into an image frame and then encoded by theauxiliary patch info compression module (338) to generate the compressedauxiliary patch information.

The patch packing module (308) is configured to map the extractedpatches onto a 2 dimensional (2D) grid while minimize the unused spaceand guarantee that every M×M (e.g., 16×16) block of the grid isassociated with a unique patch. Efficient patch packing can directlyimpact the compression efficiency either by minimizing the unused spaceor ensuring temporal consistency.

The geometry image generation module (310) can generate 2D geometryimages associated with geometry of the point cloud at given patchlocations. The texture image generation module (312) can generate 2Dtexture images associated with texture of the point cloud at given patchlocations. The geometry image generation module (310) and the textureimage generation module (312) exploit the 3D to 2D mapping computedduring the packing process to store the geometry and texture of thepoint cloud as images. In order to better handle the case of multiplepoints being projected to the same sample, each patch is projected ontotwo images, referred to as layers. In an example, geometry image isrepresented by a monochromatic frame of W×H in YUV420-8 bit format. Togenerate the texture image, the texture generation procedure exploitsthe reconstructed/smoothed geometry in order to compute the colors to beassociated with the re-sampled points.

The occupancy map module (314) can generate an occupancy map thatdescribes padding information at each unit. For example, the occupancyimage includes a binary map that indicates for each cell of the gridwhether the cell belongs to the empty space or to the point cloud. In anexample, the occupancy map uses binary information describing for eachpixel whether the pixel is padded or not. In another example, theoccupancy map uses binary information describing for each block ofpixels whether the block of pixels is padded or not.

The occupancy map generated by the occupancy map module (314) can becompressed using lossless coding or lossy coding. When lossless codingis used, the entropy compression module (334) is used to compress theoccupancy map. When lossy coding is used, the video compression module(332) is used to compress the occupancy map.

It is noted that the patch packing module (308) may leave some emptyspaces between 2D patches packed in an image frame. The image paddingmodules (316) and (318) can fill the empty spaces (referred to aspadding) in order to generate an image frame that may be suited for 2Dvideo and image codecs. The image padding is also referred to asbackground filling which can fill the unused space with redundantinformation. In some examples, a good background filling minimallyincreases the bit rate while does not introduce significant codingdistortion around the patch boundaries.

The video compression modules (322), (323), and (332) can encode the 2Dimages, such as the padded geometry images, padded texture images, andoccupancy maps based on a suitable video coding standard, such as HEVC,VVC and the like. In an example, the video compression modules (322),(323), and (332) are individual components that operate separately. Itis noted that the video compression modules (322), (323), and (332) canbe implemented as a single component in another example.

In some examples, the smoothing module (336) is configured to generate asmoothed image of the reconstructed geometry image. The smoothed imagecan be provided to the texture image generation (312). Then, the textureimage generation (312) may adjust the generation of the texture imagebased on the reconstructed geometry images. For example, when a patchshape (e.g. geometry) is slightly distorted during encoding anddecoding, the distortion may be taken into account when generating thetexture images to correct for the distortion in patch shape.

In some embodiments, the group dilation (320) is configured to padpixels around the object boundaries with redundant low-frequency contentin order to improve coding gain as well as visual quality ofreconstructed point cloud.

The multiplexer (324) can multiplex the compressed geometry image, thecompressed texture image, the compressed occupancy map, the compressedauxiliary patch information into a compressed bitstream.

FIG. 4 shows a block diagram of a V-PCC decoder (400) for decodingcompressed bitstream corresponding to point cloud frames, according tosome embodiments. In some embodiments, the V-PCC decoder (400) can beused in the communication system (100) and streaming system (200). Forexample, the decoder (210) can be configured to operate in a similarmanner as the V-PCC decoder (400). The V-PCC decoder (400) receives thecompressed bitstream, and generates reconstructed point cloud based onthe compressed bitstream.

In the FIG. 4 example, the V-PCC decoder (400) includes a de-multiplexer(432), video decompression modules (434) and (436), an occupancy mapdecompression module (438), an auxiliary patch-information decompressionmodule (442), a geometry reconstruction module (444), a smoothing module(446), a texture reconstruction module (448), and a color smoothingmodule (452).

The de-multiplexer (432) can receive and separate the compressedbitstream into compressed texture image, compressed geometry image,compressed occupancy map, and compressed auxiliary patch information.

The video decompression modules (434) and (436) can decode thecompressed images according to a suitable standard (e.g., HEVC, VVC,etc.) and output decompressed images. For example, the videodecompression module (434) decodes the compressed texture images andoutputs decompressed texture images; and the video decompression module(436) decodes the compressed geometry images and outputs thedecompressed geometry images.

The occupancy map decompression module (438) can decode the compressedoccupancy maps according to a suitable standard (e.g., HEVC, VVC, etc.)and output decompressed occupancy maps.

The auxiliary patch-information decompression module (442) can decodethe compressed auxiliary patch information according to a suitablestandard (e.g., HEVC, VVC, etc.) and output decompressed auxiliary patchinformation.

The geometry reconstruction module (444) can receive the decompressedgeometry images, and generate reconstructed point cloud geometry basedon the decompressed occupancy map and decompressed auxiliary patchinformation.

The smoothing module (446) can smooth incongruences at edges of patches.The smoothing procedure aims at alleviating potential discontinuitiesthat may arise at the patch boundaries due to compression artifacts. Insome embodiments, a smoothing filter may be applied to the pixelslocated on the patch boundaries to alleviate the distortions that may becaused by the compression/decompression.

The texture reconstruction module (448) can determine textureinformation for points in the point cloud based on the decompressedtexture images and the smoothing geometry.

The color smoothing module (452) can smooth incongruences of coloring.Non-neighboring patches in 3D space are often packed next to each otherin 2D videos. In some examples, pixel values from non-neighboringpatches might be mixed up by the block-based video codec. The goal ofcolor smoothing is to reduce the visible artifacts that appear at patchboundaries.

FIG. 5 shows a block diagram of a video decoder (510) according to anembodiment of the present disclosure. The video decoder (510) can beused in the V-PCC decoder (400). For example, the video decompressionmodules (434) and (436), the occupancy map decompression module (438)can be similarly configured as the video decoder (510).

The video decoder (510) may include a parser (520) to reconstructsymbols (521) from compressed images, such as the coded video sequence.Categories of those symbols include information used to manage operationof the video decoder (510). The parser (520) may parse/entropy-decodethe coded video sequence that is received. The coding of the coded videosequence can be in accordance with a video coding technology orstandard, and can follow various principles, including variable lengthcoding, Huffman coding, arithmetic coding with or without contextsensitivity, and so forth. The parser (520) may extract from the codedvideo sequence, a set of subgroup parameters for at least one of thesubgroups of pixels in the video decoder, based upon at least oneparameter corresponding to the group. Subgroups can include Groups ofPictures (GOPs), pictures, tiles, slices, macroblocks, Coding Units(CUs), blocks, Transform Units (TUs), Prediction Units (PUs) and soforth. The parser (520) may also extract from the coded video sequenceinformation such as transform coefficients, quantizer parameter values,motion vectors, and so forth.

The parser (520) may perform an entropy decoding/parsing operation onthe video sequence received from a buffer memory, so as to createsymbols (521).

Reconstruction of the symbols (521) can involve multiple different unitsdepending on the type of the coded video picture or parts thereof (suchas: inter and intra picture, inter and intra block), and other factors.Which units are involved, and how, can be controlled by the subgroupcontrol information that was parsed from the coded video sequence by theparser (520). The flow of such subgroup control information between theparser (520) and the multiple units below is not depicted for clarity.

Beyond the functional blocks already mentioned, the video decoder (510)can be conceptually subdivided into a number of functional units asdescribed below. In a practical implementation operating undercommercial constraints, many of these units interact closely with eachother and can, at least partly, be integrated into each other. However,for the purpose of describing the disclosed subject matter, theconceptual subdivision into the functional units below is appropriate.

A first unit is the scaler/inverse transform unit (551). Thescaler/inverse transform unit (551) receives a quantized transformcoefficient as well as control information, including which transform touse, block size, quantization factor, quantization scaling matrices,etc. as symbol(s) (521) from the parser (520). The scaler/inversetransform unit (551) can output blocks comprising sample values that canbe input into aggregator (555).

In some cases, the output samples of the scaler/inverse transform (551)can pertain to an intra coded block; that is: a block that is not usingpredictive information from previously reconstructed pictures, but canuse predictive information from previously reconstructed parts of thecurrent picture. Such predictive information can be provided by an intrapicture prediction unit (552). In some cases, the intra pictureprediction unit (552) generates a block of the same size and shape ofthe block under reconstruction, using surrounding already reconstructedinformation fetched from the current picture buffer (558). The currentpicture buffer (558) buffers, for example, partly reconstructed currentpicture and/or fully reconstructed current picture. The aggregator(555), in some cases, adds, on a per sample basis, the predictioninformation the intra prediction unit (552) has generated to the outputsample information as provided by the scaler/inverse transform unit(551).

In other cases, the output samples of the scaler/inverse transform unit(551) can pertain to an inter coded, and potentially motion compensatedblock. In such a case, a motion compensation prediction unit (553) canaccess reference picture memory (557) to fetch samples used forprediction. After motion compensating the fetched samples in accordancewith the symbols (521) pertaining to the block, these samples can beadded by the aggregator (555) to the output of the scaler/inversetransform unit (551) (in this case called the residual samples orresidual signal) so as to generate output sample information. Theaddresses within the reference picture memory (557) from where themotion compensation prediction unit (553) fetches prediction samples canbe controlled by motion vectors, available to the motion compensationprediction unit (553) in the form of symbols (521) that can have, forexample X, Y, and reference picture components. Motion compensation alsocan include interpolation of sample values as fetched from the referencepicture memory (557) when sub-sample exact motion vectors are in use,motion vector prediction mechanisms, and so forth.

The output samples of the aggregator (555) can be subject to variousloop filtering techniques in the loop filter unit (556). Videocompression technologies can include in-loop filter technologies thatare controlled by parameters included in the coded video sequence (alsoreferred to as coded video bitstream) and made available to the loopfilter unit (556) as symbols (521) from the parser (520), but can alsobe responsive to meta-information obtained during the decoding ofprevious (in decoding order) parts of the coded picture or coded videosequence, as well as responsive to previously reconstructed andloop-filtered sample values.

The output of the loop filter unit (556) can be a sample stream that canbe output to a render device as well as stored in the reference picturememory (557) for use in future inter-picture prediction.

Certain coded pictures, once fully reconstructed, can be used asreference pictures for future prediction. For example, once a codedpicture corresponding to a current picture is fully reconstructed andthe coded picture has been identified as a reference picture (by, forexample, the parser (520)), the current picture buffer (558) can becomea part of the reference picture memory (557), and a fresh currentpicture buffer can be reallocated before commencing the reconstructionof the following coded picture.

The video decoder (510) may perform decoding operations according to apredetermined video compression technology in a standard, such as ITU-TRec. H.265. The coded video sequence may conform to a syntax specifiedby the video compression technology or standard being used, in the sensethat the coded video sequence adheres to both the syntax of the videocompression technology or standard and the profiles as documented in thevideo compression technology or standard. Specifically, a profile canselect certain tools as the only tools available for use under thatprofile from all the tools available in the video compression technologyor standard. Also necessary for compliance can be that the complexity ofthe coded video sequence is within bounds as defined by the level of thevideo compression technology or standard. In some cases, levels restrictthe maximum picture size, maximum frame rate, maximum reconstructionsample rate (measured in, for example megasamples per second), maximumreference picture size, and so on. Limits set by levels can, in somecases, be further restricted through Hypothetical Reference Decoder(HRD) specifications and metadata for HRD buffer management signaled inthe coded video sequence.

FIG. 6 shows a block diagram of a video encoder (603) according to anembodiment of the present disclosure. The video encoder (603) can beused in the V-PCC encoder (300) the compresses point clouds. In anexample, the video compression module (322) and (323), and the videocompression module (332) are configured similarly to the encoder (603).

The video encoder (603) may receive images, such as padded geometryimages, padded texture images and the like, and generate compressedimages.

According to an embodiment, the video encoder (603) may code andcompress the pictures of the source video sequence (images) into a codedvideo sequence (compressed images) in real time or under any other timeconstraints as required by the application. Enforcing appropriate codingspeed is one function of a controller (650). In some embodiments, thecontroller (650) controls other functional units as described below andis functionally coupled to the other functional units. The coupling isnot depicted for clarity. Parameters set by the controller (650) caninclude rate control related parameters (picture skip, quantizer, lambdavalue of rate-distortion optimization techniques, . . . ), picture size,group of pictures (GOP) layout, maximum motion vector search range, andso forth. The controller (650) can be configured to have other suitablefunctions that pertain to the video encoder (603) optimized for acertain system design.

In some embodiments, the video encoder (603) is configured to operate ina coding loop. As an oversimplified description, in an example, thecoding loop can include a source coder (630) (e.g., responsible forcreating symbols, such as a symbol stream, based on an input picture tobe coded, and a reference picture(s)), and a (local) decoder (633)embedded in the video encoder (603). The decoder (633) reconstructs thesymbols to create the sample data in a similar manner as a (remote)decoder also would create (as any compression between symbols and codedvideo bitstream is lossless in the video compression technologiesconsidered in the disclosed subject matter). The reconstructed samplestream (sample data) is input to the reference picture memory (634). Asthe decoding of a symbol stream leads to bit-exact results independentof decoder location (local or remote), the content in the referencepicture memory (634) is also bit exact between the local encoder andremote encoder. In other words, the prediction part of an encoder “sees”as reference picture samples exactly the same sample values as a decoderwould “see” when using prediction during decoding. This fundamentalprinciple of reference picture synchronicity (and resulting drift, ifsynchronicity cannot be maintained, for example because of channelerrors) is used in some related arts as well.

The operation of the “local” decoder (633) can be the same as of a“remote” decoder, such as the video decoder (510), which has alreadybeen described in detail above in conjunction with FIG. 5. Brieflyreferring also to FIG. 5, however, as symbols are available andencoding/decoding of symbols to a coded video sequence by an entropycoder (645) and the parser (520) can be lossless, the entropy decodingparts of the video decoder (510), including and parser (520) may not befully implemented in the local decoder (633).

An observation that can be made at this point is that any decodertechnology except the parsing/entropy decoding that is present in adecoder also necessarily needs to be present, in substantially identicalfunctional form, in a corresponding encoder. For this reason, thedisclosed subject matter focuses on decoder operation. The descriptionof encoder technologies can be abbreviated as they are the inverse ofthe comprehensively described decoder technologies. Only in certainareas a more detail description is required and provided below.

During operation, in some examples, the source coder (630) may performmotion compensated predictive coding, which codes an input picturepredictively with reference to one or more previously-coded picture fromthe video sequence that were designated as “reference pictures”. In thismanner, the coding engine (632) codes differences between pixel blocksof an input picture and pixel blocks of reference picture(s) that may beselected as prediction reference(s) to the input picture.

The local video decoder (633) may decode coded video data of picturesthat may be designated as reference pictures, based on symbols createdby the source coder (630). Operations of the coding engine (632) mayadvantageously be lossy processes. When the coded video data may bedecoded at a video decoder (not shown in FIG. 6), the reconstructedvideo sequence typically may be a replica of the source video sequencewith some errors. The local video decoder (633) replicates decodingprocesses that may be performed by the video decoder on referencepictures and may cause reconstructed reference pictures to be stored inthe reference picture cache (634). In this manner, the video encoder(603) may store copies of reconstructed reference pictures locally thathave common content as the reconstructed reference pictures that will beobtained by a far-end video decoder (absent transmission errors).

The predictor (635) may perform prediction searches for the codingengine (632). That is, for a new picture to be coded, the predictor(635) may search the reference picture memory (634) for sample data (ascandidate reference pixel blocks) or certain metadata such as referencepicture motion vectors, block shapes, and so on, that may serve as anappropriate prediction reference for the new pictures. The predictor(635) may operate on a sample block-by-pixel block basis to findappropriate prediction references. In some cases, as determined bysearch results obtained by the predictor (635), an input picture mayhave prediction references drawn from multiple reference pictures storedin the reference picture memory (634).

The controller (650) may manage coding operations of the source coder(630), including, for example, setting of parameters and subgroupparameters used for encoding the video data.

Output of all aforementioned functional units may be subjected toentropy coding in the entropy coder (645). The entropy coder (645)translates the symbols as generated by the various functional units intoa coded video sequence, by lossless compressing the symbols according totechnologies such as Huffman coding, variable length coding, arithmeticcoding, and so forth.

The controller (650) may manage operation of the video encoder (603).During coding, the controller (650) may assign to each coded picture acertain coded picture type, which may affect the coding techniques thatmay be applied to the respective picture. For example, pictures oftenmay be assigned as one of the following picture types:

An Intra Picture (I picture) may be one that may be coded and decodedwithout using any other picture in the sequence as a source ofprediction. Some video codecs allow for different types of intrapictures, including, for example Independent Decoder Refresh (“IDR”)Pictures. A person skilled in the art is aware of those variants of Ipictures and their respective applications and features.

A predictive picture (P picture) may be one that may be coded anddecoded using intra prediction or inter prediction using at most onemotion vector and reference index to predict the sample values of eachblock.

A bi-directionally predictive picture (B Picture) may be one that may becoded and decoded using intra prediction or inter prediction using atmost two motion vectors and reference indices to predict the samplevalues of each block. Similarly, multiple-predictive pictures can usemore than two reference pictures and associated metadata for thereconstruction of a single block.

Source pictures commonly may be subdivided spatially into a plurality ofsample blocks (for example, blocks of 4×4, 8×8, 4×8, or 16×16 sampleseach) and coded on a block-by-block basis. Blocks may be codedpredictively with reference to other (already coded) blocks asdetermined by the coding assignment applied to the blocks' respectivepictures. For example, blocks of I pictures may be codednon-predictively or they may be coded predictively with reference toalready coded blocks of the same picture (spatial prediction or intraprediction). Pixel blocks of P pictures may be coded predictively, viaspatial prediction or via temporal prediction with reference to onepreviously coded reference picture. Blocks of B pictures may be codedpredictively, via spatial prediction or via temporal prediction withreference to one or two previously coded reference pictures.

The video encoder (603) may perform coding operations according to apredetermined video coding technology or standard, such as ITU-T Rec.H.265. In its operation, the video encoder (603) may perform variouscompression operations, including predictive coding operations thatexploit temporal and spatial redundancies in the input video sequence.The coded video data, therefore, may conform to a syntax specified bythe video coding technology or standard being used.

A video may be in the form of a plurality of source pictures (images) ina temporal sequence. Intra-picture prediction (often abbreviated tointra prediction) makes use of spatial correlation in a given picture,and inter-picture prediction makes uses of the (temporal or other)correlation between the pictures. In an example, a specific pictureunder encoding/decoding, which is referred to as a current picture, ispartitioned into blocks. When a block in the current picture is similarto a reference block in a previously coded and still buffered referencepicture in the video, the block in the current picture can be coded by avector that is referred to as a motion vector. The motion vector pointsto the reference block in the reference picture, and can have a thirddimension identifying the reference picture, in case multiple referencepictures are in use.

In some embodiments, a bi-prediction technique can be used in theinter-picture prediction. According to the bi-prediction technique, tworeference pictures, such as a first reference picture and a secondreference picture that are both prior in decoding order to the currentpicture in the video (but may be in the past and future, respectively,in display order) are used. A block in the current picture can be codedby a first motion vector that points to a first reference block in thefirst reference picture, and a second motion vector that points to asecond reference block in the second reference picture. The block can bepredicted by a combination of the first reference block and the secondreference block.

Further, a merge mode technique can be used in the inter-pictureprediction to improve coding efficiency.

According to some embodiments of the disclosure, predictions, such asinter-picture predictions and intra-picture predictions are performed inthe unit of blocks. For example, according to the HEVC standard, apicture in a sequence of video pictures is partitioned into coding treeunits (CTU) for compression, the CTUs in a picture have the same size,such as 64×64 pixels, 32×32 pixels, or 16×16 pixels. In general, a CTUincludes three coding tree blocks (CTBs), which are one luma CTB and twochroma CTBs. Each CTU can be recursively quadtree split into one ormultiple coding units (CUs). For example, a CTU of 64×64 pixels can besplit into one CU of 64×64 pixels, or 4 CUs of 32×32 pixels, or 16 CUsof 16×16 pixels. In an example, each CU is analyzed to determine aprediction type for the CU, such as an inter prediction type or an intraprediction type. The CU is split into one or more prediction units (PUs)depending on the temporal and/or spatial predictability. Generally, eachPU includes a luma prediction block (PB), and two chroma PBs. In anembodiment, a prediction operation in coding (encoding/decoding) isperformed in the unit of a prediction block. Using a luma predictionblock as an example of a prediction block, the prediction block includesa matrix of values (e.g., luma values) for pixels, such as 8×8 pixels,16×16 pixels, 8×16 pixels, 16×8 pixels, and the like.

FIG. 7 shows a block diagram of a G-PPC encoder (700) in accordance withan embodiment. The encoder (700) can be configured to receive pointcloud data and compress the point cloud data to generate a bit streamcarrying compressed point cloud data. In an embodiment, the encoder(700) can include a position quantization module (710), a duplicatedpoints removal module (712), an octree encoding module (730), anattribute transfer module (720), a level of detail (LOD) generationmodule (740), an attribute prediction module (750), a residualquantization module (760), an arithmetic coding module (770), an inverseresidual quantization module (780), an addition module (781), and amemory (790) to store reconstructed attribute values.

As shown, an input point cloud (701) can be received at the encoder(700). Positions (e.g., 3D coordinates) of the point cloud (701) areprovided to the quantization module (710). The quantization module (710)is configured to quantize the coordinates to generate quantizedpositions. The duplicated points removal module (712) is configured toreceive the quantized positions and perform a filter process to identifyand remove duplicated points. The octree encoding module (730) isconfigured to receive filtered positions from the duplicated pointsremoval module (712), and perform an octree-based encoding process togenerate a sequence of occupancy codes that describe a 3D grid ofvoxels. The occupancy codes are provided to the arithmetic coding module(770).

The attribute transfer module (720) is configured to receive attributesof the input point cloud, and perform an attribute transfer process todetermine an attribute value for each voxel when multiple attributevalues are associated with the respective voxel. The attribute transferprocess can be performed on the re-ordered points output from the octreeencoding module (730). The attributes after the transfer operations areprovided to the attribute prediction module (750). The LOD generationmodule (740) is configured to operate on the re-ordered points outputfrom the octree encoding module (730), and re-organize the points intodifferent LODs. LOD information is supplied to the attribute predictionmodule (750).

The attribute prediction module (750) processes the points according toan LOD-based order indicated by the LOD information from the LODgeneration module (740). The attribute prediction module (750) generatesan attribute prediction for a current point based on reconstructedattributes of a set of neighboring points of the current point stored inthe memory (790). Prediction residuals can subsequently be obtainedbased on original attribute values received from the attribute transfermodule (720) and locally generated attribute predictions. When candidateindices are used in the respective attribute prediction process, anindex corresponding to a selected prediction candidate may be providedto the arithmetic coding module (770).

The residual quantization module (760) is configured to receive theprediction residuals from the attribute prediction module (750), andperform quantization to generate quantized residuals. The quantizedresiduals are provided to the arithmetic coding module (770).

The inverse residual quantization module (780) is configured to receivethe quantized residuals from the residual quantization module (760), andgenerate reconstructed prediction residuals by performing an inverse ofthe quantization operations performed at the residual quantizationmodule (760). The addition module (781) is configured to receive thereconstructed prediction residuals from the inverse residualquantization module (780), and the respective attribute predictions fromthe attribute prediction module (750). By combining the reconstructedprediction residuals and the attribute predictions, the reconstructedattribute values are generated and stored to the memory (790).

The arithmetic coding module (770) is configured to receive theoccupancy codes, the candidate indices (if used), the quantizedresiduals (if generated), and other information, and perform entropyencoding to further compress the received values or information. As aresult, a compressed bitstream (702) carrying the compressed informationcan be generated. The bitstream (702) may be transmitted, or otherwiseprovided, to a decoder that decodes the compressed bitstream, or may bestored in a storage device.

FIG. 8 shows a block diagram of a G-PCC decoder (800) in accordance withan embodiment. The decoder (800) can be configured to receive acompressed bitstream and perform point cloud data decompression todecompress the bitstream to generate decoded point cloud data. In anembodiment, the decoder (800) can include an arithmetic decoding module(810), an inverse residual quantization module (820), an octree decodingmodule (830), an LOD generation module (840), an attribute predictionmodule (850), and a memory (860) to store reconstructed attributevalues.

As shown, a compressed bitstream (801) can be received at the arithmeticdecoding module (810). The arithmetic decoding module (810) isconfigured to decode the compressed bitstream (801) to obtain quantizedresiduals (if generated) and occupancy codes of a point cloud. Theoctree decoding module (830) is configured to determine reconstructedpositions of points in the point cloud according to the occupancy codes.The LOD generation module (840) is configured to re-organize the pointsinto different LODs based on the reconstructed positions, and determinean LOD-based order. The inverse residual quantization module (820) isconfigured to generate reconstructed residuals based on the quantizedresiduals received from the arithmetic decoding module (810).

The attribute prediction module (850) is configured to perform anattribute prediction process to determine attribute predictions for thepoints according to the LOD-based order. For example, an attributeprediction of a current point can be determined based on reconstructedattribute values of neighboring points of the current point stored inthe memory (860). The attribute prediction module (850) can combine theattribute prediction with a respective reconstructed residual togenerate a reconstructed attribute for the current point.

A sequence of reconstructed attributes generated from the attributeprediction module (850) together with the reconstructed positionsgenerated from the octree decoding module (830) corresponds to a decodedpoint cloud (802) that is output from the decoder (800) in one example.In addition, the reconstructed attributes are also stored into thememory (860) and can be subsequently used for deriving attributepredictions for subsequent points.

In various embodiments, the encoder (300), the decoder (400), theencoder (700), and/or the decoder (800) can be implemented withhardware, software, or combination thereof. For example, the encoder(300), the decoder (400), the encoder (700), and/or the decoder (800)can be implemented with processing circuitry such as one or moreintegrated circuits (ICs) that operate with or without software, such asan application specific integrated circuit (ASIC), field programmablegate array (FPGA), and the like. In another example, the encoder (300),the decoder (400), the encoder (700), and/or the decoder (800) can beimplemented as software or firmware including instructions stored in anon-volatile (or non-transitory) computer-readable storage medium. Theinstructions, when executed by processing circuitry, such as one or moreprocessors, causing the processing circuitry to perform functions of theencoder (300), the decoder (400), the encoder (700), and/or the decoder(800).

It is noted that the attribute prediction modules (750) and (850)configured to implement the attribute prediction techniques disclosedherein can be included in other decoders or encoders that may havesimilar or different structures from what is shown in FIG. 7 and FIG. 8.In addition, the encoder (700) and decoder (800) can be included in asame device, or separate devices in various examples.

According to some aspects of the disclosure, in some related examples(e.g., current version of the TMC13 model), a recoloring process isapplied at the encoder-side when the geometry is quantized and theduplicated positions are merged. The recolor process is relativelycomplex. For example, the recolor process relies on a k-d tree (e.g., 3dimensional tree) data structure to search for nearest neighbor(s). Thesearches based on the k-d tree can be complex and time consuming.

In a related example (e.g., a version of TMC13 model), the inputs to therecoloring algorithm can include the original positions of the originalpoints in the point cloud, attributes associated with the originalpositions and reconstructed positions (e.g., results of the geometryquantization and merging of duplicated positions). The recoloringalgorithm can transfer the attributes associated with the originalpositions to attributes associated with the reconstructed positions, forexample with minimized attribute distortions. The attributes associatedwith the reconstructed positions can form reconstructed points in areconstructed point cloud. It is noted that the recoloring algorithm cantransfer any suitable attribute, such as color, material properties,texture information, intensity attributes, reflectivity attributes,motion related attributes, modality attributes, and various otherattributes. In an example, the recoloring algorithm includes five stepsthat are described in detail in the following description.

In a first step, the original positions of the points in the pointcloud, attributes associated with the original positions and thereconstructed positions are received. In an example,(X_(i))_(i=0 . . . N-1) denote the original positions of the originalpoints in the original point cloud, N denotes the number of points inthe original point cloud, ({tilde over (X)}_(i))_(i=0 . . . N) _(rec-1)denote the reconstructed positions, and N_(rec) denotes the number ofreconstructed positions. If duplicated points exist and are merged, thenN_(rec)<N, otherwise N_(rec)=N.

In a second step, for each position {tilde over (X)}_(i) in thereconstructed point cloud, a search process is performed, for example,based on a k-d tree search, to determine X_(i)* that is a nearestneighbour in the original point cloud and a_(i)* denotes the attributevalue associated with the nearest neighbour in the original point cloud.

In a third step, for each position {tilde over (X)}_(i) in thereconstructed point cloud, search processes are performed, for example,based on a k-d tree search, to determine a set of original positions inthe original point cloud that is denoted by

⁺(i)=(X_(i) ⁺(h))_(h∈{1, . . . , H(i)}), the set of original positionsshare {tilde over (X)}_(i) as their nearest neighbour in thereconstructed point cloud. H(i) denotes the number of elements in

⁺(i), and X_(i) ⁺(h) denotes one of the elements of

⁺(i). It is noted that

⁺(i) could be empty or could have one or multiple elements.

In a fourth step,

⁺(i) is checked. When

⁺(i) is empty, the attribute value ã_(i) associated with the position{tilde over (X)}_(i) is set to a_(i)*.

In a fifth step, if

⁺(i) is not empty, the attribute value ã_(i) associated with theposition {tilde over (X)}_(i) can be calculated based on, for example(Eq. 1):

$\begin{matrix}{{\overset{\sim}{a}}_{i} = {\frac{1}{H(i)}{\sum_{h = 1}^{H(i)}{a_{i}^{+}(h)}}}} & \left( {{Eq}.1} \right)\end{matrix}$

In the related example, searches, such as k-d tree (e.g., 3 dimensionaltree) searches are used to search for nearest neighbor(s). The searchesbased on the k-d tree can be complex and time consuming. The presentdisclosure provides recoloring techniques to reduce complexity andimprove processing speed. For example, the searches of the nearestneighbors can be determined based on information stored during geometryquantization and merging of duplicated points.

The proposed methods may be used separately or combined in any order.Further, each of the methods (or embodiments), encoder, and decoder maybe implemented by processing circuitry (e.g., one or more processors orone or more integrated circuits). In one example, the one or moreprocessors execute a program that is stored in a non-transitorycomputer-readable medium.

According to some aspects of the disclosure, geometry information andthe associated attributes of a point cloud, such as color, reflectanceand the like can be separately compressed (e.g., in the Test Model 13(TMC13) model). In some embodiments, the geometry information of thepoint cloud, which includes the 3D coordinates of the points in thepoint cloud, can be processed according to quantization and merge ofduplication process. In some examples, the quantization and merge ofduplication process can based on octree partition, and the geometryinformation can be coded by an octree partition with occupancyinformation of the partitions. The attributes can be compressed based ona reconstructed geometry using, for example, prediction, lifting andregion adaptive hierarchical transform techniques techniques. Forexample, the coded geometry information can be dequantized toreconstruct the geometry information for the point cloud, such asreconstructed positions. The attributes of the original points aretransferred to the reconstructed positions and then encoded.

According to some aspects of the disclosure, a three dimensional spacecan be partitioned using octree partition. Octrees are the threedimensional analog of quadtrees in the two dimensional space. Octreepartition technique refers to the partition technique that recursivelysubdivides three dimensional space into eight octants, and an octreestructure refers to the tree structure that represents the partitions.In an example, each node in the octree structure corresponds to a threedimensional space, and the node can be an end node (no more partition,also referred to as leaf node in some examples) or a node with a furtherpartition. A partition at a node can partition the three dimensionalspace represented by the node into eight octants. In some examples,nodes corresponding to partitions of a specific node can be referred toas child nodes of the specific node.

FIG. 9 shows a diagram illustrating a partition of a 3D cube (900)(corresponding to a node) based on the octree partition techniqueaccording to some embodiments of the disclosure. The partition candivide the 3D cube (900) into eight smaller equal-sized cubes 0-7 asshown in FIG. 9.

The octree partition technique (e.g., in TMC13) can recursively dividean original 3D space into the smaller units, and the occupancyinformation of every sub-space can be encoded to represent geometrypositions.

In some embodiments (e.g., In TMC13), an octree geometry codec is used.The octree geometry codec can perform geometry encoding. In someexamples, geometry encoding is performed on a cubical box. For example,the cubical box can be an axis-aligned bounding box B that is defined bytwo points (0,0,0) and (2^(M-1), 2^(M-1), 2^(M-1)) where 2^(M-1) definesthe size of the bounding box B and M can be specified in the bitstream.

Then, an octree structure is built by recursively subdividing thecubical box. For example, the cubical box defined by the two points(0,0,0) and (2^(M-1), 2^(M-1), 2^(M-1)) is divided into 8 sub cubicalboxes, then an 8-bit code, that is referred to as an occupancy code, isgenerated. Each bit of the occupancy code is associated with a subcubical box, and the value of the bit is used to indicate whether theassociated sub cubical box contains any points of the point cloud. Forexample, value 1 of a bit indicates that the sub cubical box associatedwith the bit contains one or more points of the point cloud; and value 0of a bit indicates that the sub cubical box associated with the bitcontains no point of the point cloud.

Further, for empty sub cubical box (e.g., the value of the bitassociated with the sub cubical box is 0), no more division is appliedon the sub cubical box. When a sub cubical box has one or more points ofthe point cloud (e.g., the value of the bit associated with the subcubical box is 1), the sub cubical box is further divided into 8 smallersub cubical boxes, and an occupancy code can be generated for the subcubical box to indicate the occupancy of the smaller sub cubical boxes.In some examples, the subdivision operations can be repetitivelyperformed on non-empty sub cubical boxes until the size of the subcubical boxes is equal to a predetermined threshold, such as sizebeing 1. In some examples, the sub cubical boxes with a size of 1 (unitsize) are referred to as voxels, and the sub cubical boxes that havelarger sizes than voxels can be referred to as non-voxels.

FIG. 10 shows an example of an octree partition (1010) and an octreestructure (1020) corresponding to the octree partition (1010) accordingto some embodiments of the disclosure. FIG. 10 shows two levels ofpartitions in the octree partition (1010). The octree structure (1020)includes a node (N0) corresponding to the cubical box for octreepartition (1010). At a first level, the cubical box is partitioned into8 sub cubical boxes that are numbered 0-7 according to the numberingtechnique shown in FIG. 9. The occupancy code for the partition of thenode N0 is “10000001” in binary, which indicates the first sub cubicalbox represented by node N0-0 and the eighth sub cubical box representedby node N0-7 includes points in the point cloud and other sub cubicalboxes are empty.

Then, at the second level of partition, the first sub cubical box(represented by node N0-0) and the eighth sub cubical box (representedby node N0-7) are further respectively sub-divided into eight octants.For example, the first sub cubical box (represented by node N0-0) ispartitioned into 8 smaller sub cubical boxes that are numbered 0-7according to the numbering technique shown in FIG. 9. The occupancy codefor the partition of the node N0-0 is “00011000” in binary, whichindicates the fourth smaller sub cubical box (represented by nodeN0-0-3) and the fifth smaller sub cubical box (represented by nodeN0-0-4) includes points in the point cloud and other smaller sub cubicalboxes are empty. At the second level, the seventh sub cubical box(represented by node N0-7) is similarly partitioned into 8 smaller subcubical boxes as shown in FIG. 10.

In the FIG. 10 example, the nodes corresponding to non-empty cubicalspace (e.g., cubical box, sub cubical boxes, smaller sub cubical boxesand the like) are colored in grey, and referred to as shaded nodes.

In some embodiments, for a voxel of a unit size cubical box, point(s) inthe unit size cubical box can be quantized to, for example, the centerposition of the unit size cubical box. When there are multiple points inthe unit size cubical box, the points are merged to the center positionof the unit size cubical box. The center positions of the voxels canform reconstructed positions for reconstructed points in a reconstructedpoint cloud.

According to some aspects of the disclosure, a suitable data structure(such as a lookup table, a list, and the like) can be used to associateoriginal points in a voxel with a reconstructed position (e.g., centerposition) of the voxel. For example, an entry in a lookup table canassociate an index of a voxel with indexes of original points in thevoxel. The data structure can be stored. Thus, for each position in thereconstructed point cloud that is a center point of a voxel, theoriginal points in the voxel that are merged to the center position canbe determined based on the data structure.

According to an aspect of the disclosure, the geometry of point cloud isquantized by scalar quantization, and the scalar quantization is theonly source of geometry distortion in some examples. In someembodiments, when the octree partition is used for geometryquantization, the voxels are the unit size cubical box that eachincludes one or more original points of the point cloud, and the one ormore original points are quantized to the center point of the voxel.Thus, the one or more original points in the voxel share the same centerpoint (in the reconstructed point cloud) of the voxel as the nearestneighbor, the set

⁺(i) of each position in reconstructed point cloud is not empty.Therefore, the fourth step in the recoloring algorithm is not needed.

FIG. 11 shows a diagram in one-dimension (1D) to illustrate derivationtechniques that can be used in three-dimension (3D) to determine the setof points sharing the reconstructed position (

⁺(i)) during geometry quantization. The diamond-shaped points 1-8 arethe original points and the round black dots R1-R3 are reconstructedpositions for reconstructed points (also referred to as quantized pointsin some examples).

In an example, the quantization step is q, so all the points in therange [(n−½)q,(n+½)q) can be be quantized to the position nq, for n=0,±1, ±2, . . . . For example, the original points 1-3 are in a range of[−3q/2, −q/2) and are quantized to the reconstructed position R1, theoriginal points 4-5 are in a range of [−q/2, q/2) and are quantized tothe reconstructed position R2, the original points 6-8 are in a range of[q/2, 3q/2) and are quantized to the reconstructed position R3.Therefore, the nearest neighbor of an original point in thereconstructed point cloud is the reconstructed position. In other words,the set

⁺(i) of each reconstructed position is the original points that arequantized to the reconstructed position. Thus, in some embodiments, asuitable data structure is used to store the set

⁺(i) associated with the reconstructed position. Then, based on the datastructure, for the reconstructed position, the set

⁺(i) can be quickly determined without additional k-d tree search.

Based on the quantization results stored in the suitable data structure,the recoloring algorithm can be simplified. For example, the attributevalue of each quantized position is assigned based on the attributevalues at original positions that are quantized to the quantizedposition. Specifically, in some examples, the recoloring algorithm caninclude 3 steps that are described in detail in the followingdescription.

In a first step, the original positions of the points in the pointcloud, attributes associated with the original positions and thereconstructed positions are received. In an example,(X_(i))_(i=0 . . . N-1) denote the original positions of the points inthe original point cloud, N denotes the number of points in the originalpoint cloud, ({tilde over (X)}_(i))_(i=0 . . . N) _(rec) ₋₁ denote thereconstructed positions of the reconstructed point cloud, and N_(rec)denotes the number of reconstructed positions in the reconstructed pointcloud. If duplicated points exist and are merged, then N_(rec)<N,otherwise N_(rec)=N.

In a second step, for each reconstructed position {tilde over (X)}_(i)in the reconstructed point cloud, the data structure that stores a setof original positions in association with each reconstructed positioncan be accessed based on the reconstructed position to determine the setof original positions

(i)=(X_(i)(h))_(h∈{1, . . . , H(i)}), original positions in the set arequantized and dequantized to {tilde over (X)}_(i). H(i) denotes thenumber of elements in

(i), and X_(i)(h) denotes one of the elements of

(i).

(i) can be obtained during the geometry quantization process.

In a third step, the attribute value ã_(i) associated with thereconstructed position {tilde over (X)}_(i) can be obtained fromattributes values associated with the original positions in the set

(i).

Various techniques can be used to calculate attribute value ã_(i).

In an embodiment, an average of the attributes values is calculated asthe attribute value ã_(i) for the reconstructed position. In an example,a_(i)(h) denotes an attribute value associated with X_(i)(h), theattribute value ã_(i) associated with the reconstructed point {tildeover (X)}_(i) can be calculated using (Eq. 2):

$\begin{matrix}{{\overset{\sim}{a}}_{i} = {\frac{1}{H(i)}{\sum_{h = 1}^{H(i)}{a_{i}(h)}}}} & \left( {{Eq}.2} \right)\end{matrix}$

In another embodiment, ã_(i) is calculated by the weighted average ofthe attributes, such as using (Eq. 3):

$\begin{matrix}{{\overset{\sim}{a}}_{i} = {\frac{1}{H(i)}{\sum_{h = 1}^{H(i)}{{w_{i}(h)} \cdot {a_{i}(h)}}}}} & \left( {{Eq}.3} \right)\end{matrix}$

where Σ_(h=1) ^(H(i))w_(i)(h)=1, w_(i)(h)≥0 and denotes the weight forattribute at a corresponding position, which can be inverselyproportional to the distance between the original position X_(i)(h) andreconstructed position {tilde over (X)}_(i). The distance between theoriginal position X_(i)(h) and reconstructed position {tilde over(X)}_(i) can be evaluated by any suitable distance measure. In anexample, the distance is a spatial distance, such as Euclidean distance.

In another embodiment, ã_(i) is assigned by the attribute value of anearest original position in

(i) to the reconstructed position {tilde over (X)}_(i). When multipleoriginal positions have the same nearest distance to the reconstructedposition {tilde over (X)}_(i), a median attribute value or a meanattribute value of the attribute values of the multiple originalpositions can be assigned to ã_(i). The distance between the originalposition X_(i)(h) and reconstructed position {tilde over (X)}_(i) can beevaluated by any other distance measure. In an example, the distance isa spatial distance, such as Euclidean distance.

FIG. 12 shows a flow chart outlining a process (1200) according to anembodiment of the disclosure. The process (1200) can be used during acoding process for point clouds. In various embodiments, the process(1200) is executed by processing circuitry, such as the processingcircuitry in the terminal devices (110), the processing circuitry thatperforms functions of the encoder (203) and/or the decoder (201), theprocessing circuitry that performs functions of the encoder (300), thedecoder (400), the encoder (700), and/or the decoder (800), and thelike. In some embodiments, the process (1200) is implemented in softwareinstructions, thus when the processing circuitry executes the softwareinstructions, the processing circuitry performs the process (1200). Theprocess starts at (S1201) and proceeds to (S1210).

At (S1210), one or more original points in a point cloud that areassociated with a reconstructed position are determined. Positions ofthe one or more original points are reconstructed, according to ageometry quantization, to the reconstructed position.

In some examples, octree partition can be performed for coding geometryinformation. The octree partition can partition a space of the pointcloud into voxels. The one or more original points are in a voxel, andare associated with the reconstructed position for representing thevoxel. The geometry information of the one or more original points canbe reconstructed (e.g., quantized and dequantized) to the reconstructedposition. In an embodiment, a data structure that associates the one ormore original points with the reconstructed position is stored. The datastructure can be accessed based on the reconstructed position toretrieve the one or more original points associated with thereconstructed position.

At (S1220), an attribute value for the reconstructed position isdetermined based on attribute information of the one or more originalpoints.

In an embodiment, an average of attribute values of multiple originalpoints is calculated as the determined attribute value for thereconstructed position when multiple original points are associated withthe reconstructed position.

In some examples, an attribute value of an original point in themultiple original points can be weighed based on an inverse of adistance between the original point and the reconstructed position.

In another embodiment, a weighted average of the attribute values of themultiple original points is calculated as the determined attribute valuefor the reconstructed position. In an example, an attribute value of anoriginal point in the multiple original points is weighted based on aninverse of a distance between the original point and the reconstructedposition.

In another embodiment, a specific attribute value of a nearest point inmultiple original points is assigned to be the determined attributevalue for the reconstructed position in response to the multipleoriginal points being associated with the reconstructed position. In anexample, a median attribute value among attribute values for nearestpoints is selected in response to an existing of multiple nearest pointshaving a same shortest distance to the reconstructed position. Inanother example, a mean attribute value of attribute values for nearestpoints is calculated in response to an existing of multiple nearestpoints having a same shortest distance to the reconstructed position.

At (S1230), texture of the point cloud is encoded with the reconstructedposition having the determined attribute value. In an example, attributevalues for the reconstructed positons are compressed and included in acoded bitstream for the point cloud as the texture information of thepoint cloud. Then, the process proceeds to (S1299) and terminates.

The techniques described above, can be implemented as computer softwareusing computer-readable instructions and physically stored in one ormore computer-readable media. For example, FIG. 13 shows a computersystem (1300) suitable for implementing certain embodiments of thedisclosed subject matter.

The computer software can be coded using any suitable machine code orcomputer language, that may be subject to assembly, compilation,linking, or like mechanisms to create code comprising instructions thatcan be executed directly, or through interpretation, micro-codeexecution, and the like, by one or more computer central processingunits (CPUs), Graphics Processing Units (GPUs), and the like.

The instructions can be executed on various types of computers orcomponents thereof, including, for example, personal computers, tabletcomputers, servers, smartphones, gaming devices, internet of thingsdevices, and the like.

The components shown in FIG. 13 for computer system (1300) are exemplaryin nature and are not intended to suggest any limitation as to the scopeof use or functionality of the computer software implementingembodiments of the present disclosure. Neither should the configurationof components be interpreted as having any dependency or requirementrelating to any one or combination of components illustrated in theexemplary embodiment of a computer system (1300).

Computer system (1300) may include certain human interface inputdevices. Such a human interface input device may be responsive to inputby one or more human users through, for example, tactile input (such as:keystrokes, swipes, data glove movements), audio input (such as: voice,clapping), visual input (such as: gestures), olfactory input (notdepicted). The human interface devices can also be used to capturecertain media not necessarily directly related to conscious input by ahuman, such as audio (such as: speech, music, ambient sound), images(such as: scanned images, photographic images obtain from a still imagecamera), video (such as two-dimensional video, three-dimensional videoincluding stereoscopic video).

Input human interface devices may include one or more of (only one ofeach depicted): keyboard (1301), mouse (1302), trackpad (1303), touchscreen (1310), data-glove (not shown), joystick (1305), microphone(1306), scanner (1307), camera (1308).

Computer system (1300) may also include certain human interface outputdevices. Such human interface output devices may be stimulating thesenses of one or more human users through, for example, tactile output,sound, light, and smell/taste. Such human interface output devices mayinclude tactile output devices (for example tactile feedback by thetouch-screen (1310), data-glove (not shown), or joystick (1305), butthere can also be tactile feedback devices that do not serve as inputdevices), audio output devices (such as: speakers (1309), headphones(not depicted)), visual output devices (such as screens (1310) toinclude CRT screens, LCD screens, plasma screens, OLED screens, eachwith or without touch-screen input capability, each with or withouttactile feedback capability—some of which may be capable to output twodimensional visual output or more than three dimensional output throughmeans such as stereographic output; virtual-reality glasses (notdepicted), holographic displays and smoke tanks (not depicted)), andprinters (not depicted).

Computer system (1300) can also include human accessible storage devicesand their associated media such as optical media including CD/DVD ROM/RW(1320) with CD/DVD or the like media (1321), thumb-drive (1322),removable hard drive or solid state drive (1323), legacy magnetic mediasuch as tape and floppy disc (not depicted), specialized ROM/ASIC/PLDbased devices such as security dongles (not depicted), and the like.

Those skilled in the art should also understand that term “computerreadable media” as used in connection with the presently disclosedsubject matter does not encompass transmission media, carrier waves, orother transitory signals.

Computer system (1300) can also include an interface to one or morecommunication networks. Networks can for example be wireless, wireline,optical. Networks can further be local, wide-area, metropolitan,vehicular and industrial, real-time, delay-tolerant, and so on. Examplesof networks include local area networks such as Ethernet, wireless LANs,cellular networks to include GSM, 3G, 4G, 5G, LTE and the like, TVwireline or wireless wide area digital networks to include cable TV,satellite TV, and terrestrial broadcast TV, vehicular and industrial toinclude CANBus, and so forth. Certain networks commonly require externalnetwork interface adapters that attached to certain general purpose dataports or peripheral buses (1349) (such as, for example USB ports of thecomputer system (1300)); others are commonly integrated into the core ofthe computer system (1300) by attachment to a system bus as describedbelow (for example Ethernet interface into a PC computer system orcellular network interface into a smartphone computer system). Using anyof these networks, computer system (1300) can communicate with otherentities. Such communication can be uni-directional, receive only (forexample, broadcast TV), uni-directional send-only (for example CANbus tocertain CANbus devices), or bi-directional, for example to othercomputer systems using local or wide area digital networks. Certainprotocols and protocol stacks can be used on each of those networks andnetwork interfaces as described above.

Aforementioned human interface devices, human-accessible storagedevices, and network interfaces can be attached to a core (1340) of thecomputer system (1300).

The core (1340) can include one or more Central Processing Units (CPU)(1341), Graphics Processing Units (GPU) (1342), specialized programmableprocessing units in the form of Field Programmable Gate Areas (FPGA)(1343), hardware accelerators for certain tasks (1344), and so forth.These devices, along with Read-only memory (ROM) (1345), Random-accessmemory (1346), internal mass storage such as internal non-useraccessible hard drives, SSDs, and the like (1347), may be connectedthrough a system bus (1348). In some computer systems, the system bus(1348) can be accessible in the form of one or more physical plugs toenable extensions by additional CPUs, GPU, and the like. The peripheraldevices can be attached either directly to the core's system bus (1348),or through a peripheral bus (1349). Architectures for a peripheral businclude PCI, USB, and the like.

CPUs (1341), GPUs (1342), FPGAs (1343), and accelerators (1344) canexecute certain instructions that, in combination, can make up theaforementioned computer code. That computer code can be stored in ROM(1345) or RAM (1346). Transitional data can be also be stored in RAM(1346), whereas permanent data can be stored for example, in theinternal mass storage (1347). Fast storage and retrieve to any of thememory devices can be enabled through the use of cache memory, that canbe closely associated with one or more CPU (1341), GPU (1342), massstorage (1347), ROM (1345), RAM (1346), and the like.

The computer readable media can have computer code thereon forperforming various computer-implemented operations. The media andcomputer code can be those specially designed and constructed for thepurposes of the present disclosure, or they can be of the kind wellknown and available to those having skill in the computer software arts.

As an example and not by way of limitation, the computer system havingarchitecture (1300), and specifically the core (1340) can providefunctionality as a result of processor(s) (including CPUs, GPUs, FPGA,accelerators, and the like) executing software embodied in one or moretangible, computer-readable media. Such computer-readable media can bemedia associated with user-accessible mass storage as introduced above,as well as certain storage of the core (1340) that are of non-transitorynature, such as core-internal mass storage (1347) or ROM (1345). Thesoftware implementing various embodiments of the present disclosure canbe stored in such devices and executed by core (1340). Acomputer-readable medium can include one or more memory devices orchips, according to particular needs. The software can cause the core(1340) and specifically the processors therein (including CPU, GPU,FPGA, and the like) to execute particular processes or particular partsof particular processes described herein, including defining datastructures stored in RAM (1346) and modifying such data structuresaccording to the processes defined by the software. In addition or as analternative, the computer system can provide functionality as a resultof logic hardwired or otherwise embodied in a circuit (for example:accelerator (1344)), which can operate in place of or together withsoftware to execute particular processes or particular parts ofparticular processes described herein. Reference to software canencompass logic, and vice versa, where appropriate. Reference to acomputer-readable media can encompass a circuit (such as an integratedcircuit (IC)) storing software for execution, a circuit embodying logicfor execution, or both, where appropriate. The present disclosureencompasses any suitable combination of hardware and software.

While this disclosure has described several exemplary embodiments, thereare alterations, permutations, and various substitute equivalents, whichfall within the scope of the disclosure. It will thus be appreciatedthat those skilled in the art will be able to devise numerous systemsand methods which, although not explicitly shown or described herein,embody the principles of the disclosure and are thus within the spiritand scope thereof.

What is claimed is:
 1. A method for point cloud coding, comprising:determining a plurality of original points in a point cloud that isquantized to a reconstructed position; determining, by a processor, anattribute value of the reconstructed position based on attributeinformation of the plurality of original points; and encoding the pointcloud based on the determined attribute value of the reconstructedposition.
 2. The method of claim 1, further comprising: storing, a datastructure that associates the plurality of original points with thereconstructed position according to a geometry quantization.
 3. Themethod of claim 2, further comprising: performing an octree partitionthat partitions a space of the point cloud into voxels; and associatingthe plurality of original points that is positioned in a voxel with thereconstructed position for representing the voxel.
 4. The method ofclaim 2, further comprising: accessing the data structure based on thereconstructed position to retrieve the plurality of original pointsquantized to the reconstructed position.
 5. The method of claim 1,wherein the attribute value of the reconstructed position is an averageof attribute values of the plurality of original points.
 6. The methodof claim 5, wherein the average of the attribute values of the pluralityof original points is a weighted average of the attribute values of theplurality of original points.
 7. The method of claim 6, wherein aweighting of an attribute value of an original point in the plurality oforiginal points is based on an inverse of a distance between theoriginal point and the reconstructed position.
 8. The method of claim 1,wherein the attribute value of the reconstructed position is a specificattribute value of a nearest point in the plurality of original points.9. The method of claim 8, wherein the attribute value of thereconstructed position is a median attribute value among attributevalues for nearest points of the plurality of original points having asame shortest distance to the reconstructed position.
 10. The method ofclaim 8, wherein the attribute value of the reconstructed position is amean attribute value of attribute values for nearest points of theplurality of original points having a same shortest distance to thereconstructed position.
 11. An apparatus for point cloud coding,comprising: processing circuitry configured to: determine a plurality oforiginal points in a point cloud that is quantized to a reconstructedposition; determine an attribute value of the reconstructed positionbased on attribute information of the plurality of original points; andencode the point cloud based on the determined attribute value of thereconstructed position.
 12. The apparatus of claim 11, furthercomprising: a memory configured to store a data structure thatassociates the plurality of original points with the reconstructedposition according to a geometry quantization.
 13. The apparatus ofclaim 12, wherein the processing circuitry is configured to: perform anoctree partition that partitions a space of the point cloud into voxels;and associate the plurality of original points that is positioned in avoxel with the reconstructed position for representing the voxel. 14.The apparatus of claim 12, wherein the processing circuitry isconfigured to: access the data structure based on the reconstructedposition to retrieve the plurality of original points quantized to thereconstructed position.
 15. The apparatus of claim 11, wherein theattribute value of the reconstructed position is an average of attributevalues of the plurality of original points.
 16. The apparatus of claim15, wherein the attribute value of the reconstructed position is aweighted average of the attribute values of the plurality of originalpoints.
 17. The apparatus of claim 16, wherein a weight of an attributevalue of an original point in the plurality of original points is basedon an inverse of a distance between the original point and thereconstructed position.
 18. The apparatus of claim 11, wherein theattribute value of the reconstructed position is a specific attributevalue of a nearest point in the plurality of original points.
 19. Theapparatus of claim 18, wherein the attribute value of the reconstructedposition is a median attribute value among attribute values for nearestpoints of the plurality of original points having a same shortestdistance to the reconstructed position.
 20. The apparatus of claim 18,wherein the attribute value of the reconstructed position is a meanattribute value of attribute values for nearest points of the pluralityof original points having a same shortest distance to the reconstructedposition.